👍 294
09/07 08:00
Language models may need to internalize information that arrives over time and retain it through many subsequent updates. To study this challenge, we introduce long-horizon memorization, a setting in which a model learns 100 query-answer tasks through continual supervised fine-tuning without retaini
中文介绍 提出长程记忆(long-horizon memorization)基准,研究模型在持续监督微调100个问答任务后的记忆保持。探索多种持续学习机制的组合效果,验证组合策略相比单一方法能显著降低灾难性遗忘。
👍 113
09/15 08:00
Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems model players and game dynamics, support design and development, adapt player-facing experiences at runtime, and evaluate resulting artifacts. Yet thes
中文介绍 综述基础模型时代的游戏AI全景,涵盖玩法建模、玩家建模、关卡设计、运行时个性化及生成物评估。分析从训练数据到部署的各阶段技术进展与开放挑战,指出跨生命周期统一建模的不足。
👍 102
09/12 08:00
Realtime spoken interaction demands deep reasoning, prompt responses, and fluid turn-taking. We present StepAudio 3 Realtime, an audio-language foundation model organized around a continuous listen-converse-think-act loop. Deep Perception captures rich acoustic cues to interpret user intent, while S
中文介绍 介绍StepAudio 3 Realtime实时语音交互模型,基于连续监听-对话-思考-行动循环架构。Deep Perception模块捕捉声学线索理解用户意图,整体实现深度推理与流畅的实时语音交互。
👍 88
09/10 08:00
Recursive self-improvement (RSI) enables AI systems to turn experience and feedback into persistent changes that improve both their capabilities and the process of future improvement. We first use the Headroom-Closed Index (HCI) to reveal the problems of existing LLMs, then introduce the RSI concept
中文介绍 提出递归自我改进(RSI)框架,使AI将经验与反馈转化为持久能力变化。先用Headroom-Closed Index揭示现有LLM局限,再定义RSI的概念、机制与评估方法,展望完全自主的自我改进智能。
👍 76
09/11 08:00
We introduce StepAudio 3 Music, a large-scale, long-form music generation model that supports explicit musical planning and open-domain text-controlled generation. The StepAudio Music Tokenizer represents audio as a 50-Hz stream from a 65536-entry single codebook, using semantically informed self-su
中文介绍 提出StepAudio 3 Music大规模长音乐生成模型,支持显式音乐规划与开放域文本控制。采用50Hz采样率、65536单码本音频Tokenizer,结合语义信息化的自监督训练提升生成质量。
👍 70
09/16 08:00
Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience-a challenge we call the sci
中文介绍 提出ScienceIDE框架,将科学代码仓库转化为Agent可学习的环境。解决工具链碎片化、领域约定隐含、正确性标准专业化导致的「科学知识学习难题」,使Agent从可执行科学代码中获取可靠训练经验。
👍 59
09/11 08:00
When we speak of recursive self-improvement (RSI), are we speaking of a phenomenon, a mechanism, or a prospect? Towards autonomous and evolving intelligence, RSI is being claimed at many scales, while no single framework that formally describes these emerging instances exists. Its counterpart in the
中文介绍 提出统一的形式化框架,将迭代策略改进与递归自我改进(RSI)纳入同一理论体系。分析RSI在现象、机制、愿景三个层面的含义,为理解自主进化智能提供形式化基础。
👍 48
09/15 08:00
Reliable confidence estimation is increasingly central to the trustworthy deployment of language models: a calibrated estimate of the probability that an output is correct decides what to ship, what to escalate, and what to retry. Existing confidence estimators, however, share one design premise: th
中文介绍 提出基于经验的置信度估计方法,从推理扩展到Agent场景。打破现有置信估计器统一依赖单一设计前提的局限,利用积累的经验数据校准输出正确概率,支持部署中的决策与升级策略。
👍 44
09/16 08:00
Coding agents are typically evaluated with desired behavior specified through issues or instructions. In practical web development, however, agents may need to infer behavior from working software and implement it in an incomplete application. We introduce ProgramDistill, a benchmark evaluating codi
中文介绍 提出ProgramDistill基准,评估编码Agent从交互式Web应用中推断行为并实现在不完整应用中的能力。区别于基于issue/指令的传统评测,聚焦参考引导的可验证SWE任务,覆盖更真实的开发场景。
👍 39
09/16 08:00
Autonomous research loops such as AutoResearch show that one coding agent can improve a training setup unattended. Run several of them and each session starts from scratch, so more agents tend to mean more duplicated search rather than more discovery. Agora is a shared memory for such agents: resear
中文介绍 提出Agora共享内存系统,基于Git实现多个AutoResearch代理间的经验共享。解决多Agent各自从头开始导致搜索重复的问题,使协同研究的边际收益随Agent数量增加而提升而非浪费。
👍 30
09/15 08:00
Large language model (LLM) trading agents can combine market data, news, and executable analysis, but their behavior is often controlled by static hand-written tool-use policies that are fixed before deployment. This limits their ability to adapt how they gather evidence, invoke tools, verify signal
中文介绍 提出EvolveTrade,通过经验驱动的策略精炼实现LLM交易Agent的自进化。突破静态手写工具策略的部署前固定限制,使Agent能自适应调整证据收集、工具调用与信号验证方式以提升交易表现。
👍 25
09/14 08:00
Scientific agents contribute to hypothesis discovery by synthesizing evidence, assessing proposals, and developing new explanations. Recent systems combine scientific agents with evolutionary search through critique, comparison, and revision. However, how different forms of agent collaboration affec
中文介绍 提出HypoEvolve框架,将遗传算法引入多Agent LLM协作中用于科学假设发现。系统研究不同Agent协作形式(批判、比较、修订)对进化搜索的影响,提升假设生成的多样性与有效性。
👍 22
09/16 08:00
Direct preference alignment methods are widely used to align large language models (LLMs) with human preferences because of their computational and memory efficiency. However, likelihood displacement motivates alternative ways to extract information from preference pairs with small likelihood margin
中文介绍 提出LLM偏好对齐的零阶范式,针对DPO等方法在似然边际较小时信息利用不足的问题,设计从偏好对中高效提取对齐信号的新方法,兼具计算与内存效率优势。
👍 20
09/15 08:00
We introduce and release ScienceBuddy, an interactive scientific research workspace that brings continually improving scientific agents into researchers' everyday workflows. ScienceBuddy supports researchers in carrying out scientific tasks while transforming their requests, feedback, and execution
中文介绍 推出ScienceBuddy交互式科研工作台,实现「递归中的递归自我改进」的科学Agent。将科研人员的请求、反馈与执行过程转化为持续改进的数据,使Agent在日常科研流程中不断进化。
👍 17
09/14 08:00
Embodied navigation requires agents to interpret visual observations, accumulate spatial knowledge, and execute actions to follow instructions or locate objects. Training-based methods face generalization challenges, while training-free methods exploit multimodal large language models (MLLMs) but of
中文介绍 提出HarnessVLN无训练具身导航框架,统一多种导航任务。利用Agent Harness机制调度多模态大模型(MLLM),克服训练方法泛化差与免训练方法性能有限的局限,实现零样本导航。
👍 14
09/16 08:00
Enabling robots to adapt to unfamiliar environments as readily as humans remains a moonshot goal of embodied AI. No finite collection of demonstrations can cover every task and situation a robot will encounter, making the ability to learn from context at deployment essential for generalization. Such
中文介绍 探索VLM Agent在机器人部署时的上下文学习能力。解决有限演示无法覆盖所有任务场景的问题,使机器人能在陌生环境中从上下文即时学习,推动具身AI向人类级泛化能力迈进。
👍 10
09/14 08:00
Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve execution mechanisms from experience. However, generalizable harness RSI remains challenging. First, evolving harnesses on evaluation benchmarks or their sub
中文介绍 提出ModularRSI模块化递归自我改进框架,针对Agent Harness的可泛化RSI难题。解决在评测基准上演化Harness导致的过拟合问题,设计可迁移、模块化的Harness改进机制。
👍 9
09/16 08:00
Intelligent systems that act in the world require image understanding that is both comprehensive and spatially grounded. Current vision-language models (VLMs) can generate fluent and detailed image captions, but reliably associating them with image pixels remains challenging. Existing methods that c
中文介绍 提出PANORAMA全景式图像描述方法,通过Mask候选选择实现像素级空间接地。解决现有VLM生成流畅描述但难以可靠关联图像像素的问题,提升视觉理解的空间一致性与完整性。
👍 9
09/13 08:00
Training a Mixture-of-Experts (MoE) model at long context or large batch size fails as soon as any one component's peak allocation exceeds device memory, so the target is every peak at once, not the average footprint. Four are left unbounded by the parallelism plans in common use, and each grows dif
中文介绍 提出长上下文MoE训练中内存峰值平峰化方法。针对任意单组件峰值超设备显存即导致训练失败的问题,处理并行方案中四个未受约束的内存增长点,实现同时控制所有峰值而非平均占用。
👍 9
09/11 08:00
We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call this the Matthew Effect in RL for LLMs, after the phenomenon of cumul
中文介绍 揭示LLM中RL训练的马太效应:RL对模型已擅长的问题改善显著,对难题改善甚微。提出「永不放弃」训练策略,通过针对性优化提升RL在困难问题上的学习收益,均衡数据集各难度表现。